This paper is in the following e-collection/theme issue:

Patient-Reported Outcome Measures (PROMs) (372) Decision Support for Health Professionals (1539) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (215) Machine Learning (2076)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42384, first published .
Psychometric Properties of a Machine Learning–Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study

Psychometric Properties of a Machine Learning–Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study

Psychometric Properties of a Machine Learning–Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study

Authors of this article:

Virginie Korb-Savoldelli1, 2 Author Orcid Image ;   Yohann Tran3, 4 Author Orcid Image ;   Germain Perrin1, 5 Author Orcid Image ;   Justine Touchard1 Author Orcid Image ;   Jean Pastre6 Author Orcid Image ;   Adrien Borowik1 Author Orcid Image ;   Corine Schwartz1 Author Orcid Image ;   Aymeric Chastel1 Author Orcid Image ;   Eric Thervet7, 8 Author Orcid Image ;   Michel Azizi4, 9 Author Orcid Image ;   Laurence Amar4, 9 Author Orcid Image ;   Benjamin Kably4, 10 Author Orcid Image ;   Armelle Arnoux3, 4, 5 Author Orcid Image ;   Brigitte Sabatier1, 2, 5 Author Orcid Image
Article Authors Cited by (7) Tweetations (6) Metrics

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  1. Van Coillie S, Prévot J, Sánchez-Ramón S, Lowe D, Borg M, Autran B, Segundo G, Pecoraro A, Garcelon N, Boersma C, Silva S, Drabwell J, Quinti I, Meyts I, Ali A, Burns S, van Hagen M, Pergent M, Mahlaoui N. Charting a course for global progress in PIDs by 2030 — proceedings from the IPOPI global multi-stakeholders’ summit (September 2023). Frontiers in Immunology 2024;15 View
  2. Zavaleta-Monestel E, Monge Bogantes L, Chavarría-Rodríguez S, Arguedas-Chacón S, Bastos-Soto N, Villalobos-Madriz J. Artificial Intelligence Tools That Improve Medication Adherence in Patients With Chronic Noncommunicable Diseases: An Updated Review. Cureus 2025 View
  3. Le Berre C, Jairath V, Panaccione R, Bourreille A, Magro F, Danese S, Peyrin-Biroulet L. Artificial Intelligence for Clinical Trial Facilitation, Lessons for Inflammatory Bowel Disease: A Scoping Review. Clinical Gastroenterology and Hepatology 2025;23(13):2399 View
  4. Rhudy C, Johnson J, Perry C, Bumgardner C, Wesley M, Fardo D, Barrett T, Talbert J. Machine learning approaches to predicting medication nonadherence: a scoping review. International Journal of Medical Informatics 2025;204:106082 View
  5. Le Bozec A, Cohen S, Montani D, Boucly A, Guignabert C, Roche A, Al Kahf S, Beurnier A, Sitbon O, Jaïs X, Jevnikar M, Humbert M, Savale L, Chaumais M. Diuretic adherence in patients with pre-capillary pulmonary hypertension: insights from the PHARE study. ERJ Open Research 2025;11(6):00110-2025 View

Conference Proceedings

  1. Abhang S, Tarambale M, Esnaashariyeh A, Shete P, Rao Jallepalli V, Rai V, Prajapati T. 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). Machine Learning-Based Monitoring and Prognosis of Chronic Kidney Disease Patients View
  2. Ghozali M. 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). Predicting Patient Adherence in Healthcare using Artificial Intelligence and Machine Learning Techniques: A Narrative Review View